Minimum variance beamforming combined with adaptive coherence weighting applied to medical ultrasound imaging

Abstract

Currently, the nonadaptive delay-and-sum (DAS) beamformer is used in medical ultrasound imaging. However, due to its data-independent nature, DAS leads to images with limited resolution and contrast. In this paper, an adaptive minimum variance (MV)-based beamformer that combines the MV and coherence factor (CF) weighting is introduced and adapted to medical ultrasound imaging. MV-adaptive beamformers can improve the image quality in terms of resolution and sidelobes by suppressing off-axis signals, while keeping onaxis ones. In addition, CF weighting can improve contrast and sidelobes by emphasizing the in-phase signals and reducing the out-of-phase ones. Combining MV and CF weighting results in simultaneous improvement of imaging resolution and contrast, outperforming both DAS and MV beamformers. In addition, because of the power of CF in reducing the focusing errors, the proposed method presents satisfactory robustness against sound velocity inhomogeneities, outperforming the regularized MV beamformer. The excellent performance of the proposed beamforming approach is demonstrated by several simulated examples.

Special Issue on Ultrafast Ultrasound Imaging and Its Applications

“…On the other hand, the computational complexity of the minimum variance beamformer is very high, and developments of efficient implementations of the minimum variance beamformer are still ongoing [12,13]. In addition, various studies on improvement in the performance of the minimum variance beamformer have been conducted [14,15].…”

A Sibelobe Suppressing Beamformer for Coherent Plane Wave Compounding

“…However, the predefined weighting coefficients result in a wider mainlobe and thus reduce the lateral resolution which is intolerable in PWI. Adaptive beamformers [10], such as the Capon beamformer or minimum variance (MV) beamformer [11], have been proposed to compute the optimal weighting coefficients by using the information from the received raw channel data continually. Though effective for sidelobe rejection, this method also has several drawbacks for PWI.…”

Cavitation Mapping

“…MV beamforming was initially used in radar, radio communications, and other far-field, non-correlated narrowband signal areas. In recent years, it has begun to be applied to near-field, highly correlated broadband ultrasound imaging (Wang et al 2005;Asl and Mahloojifar 2009;Synnevag et al 2007). Also, spatial smoothing and diagonal loading have been introduced to remove the strong correlation of ultrasonic signals and improve the robustness of the algorithm (Synnevag et al 2007).…”

“…Also, spatial smoothing and diagonal loading have been introduced to remove the strong correlation of ultrasonic signals and improve the robustness of the algorithm (Synnevag et al 2007). CF weighting (Asl and Mahloojifar 2009) is another adaptive side lobe reduction method that works through weighting the image pixel by the corresponding CF (Asl and Mahloojifar 2009), in which the CF (Hollman et al 1999) is initially used as a focusing index to indicate the focusing quality. Hu et al (2015) combined plane wave transmission, MV beamforming, and CF weighting to achieve a plane wave MVCF-based UACM, as a representative of adaptive beamforming-based UACM, to image cavitation bubbles with a high SNR and high spatial-temporal resolution.…”